Voice and Cough as Digital Biomarkers (2025–2026): Linguistic Equity, Privacy, and the Promise of Acoustic Diagnostics in Global Health

Recent advances in artificial intelligence and mobile sensing have renewed interest in the human voice and cough as non-invasive, low-cost biomarkers of disease. While respiratory, neurological, and psychiatric applications have shown moderate-to-high diagnostic accuracies in classification studies, the translation of these tools from proof-of-concept into routine clinical practice remains incomplete. This narrative review synthesizes the 2025–2026 literature across five domains: (a) the biophysiological foundations that make voice and cough acoustically informative, (b) clinical evidence spanning respiratory, neurodegenerative, mental-health, cardiometabolic, pediatric, and laryngeal use cases, (c) the growing body of work on linguistic equity and structural bias in speech-based systems, (d) privacy and adversarial security concerns specific to voice as biometric data, and (e) deployment prospects in low- and middle-income countries, with tuberculosis screening as the most advanced use case. The analysis reveals that no single disease domain has reached the level of large-scale, independently validated deployment, though respiratory and laryngeal applications are closest. Structural language bias—particularly against speakers of tonal languages—remains a first-order obstacle, and current speaker de-identification systems demonstrably leak identity information. These findings point to three conditions for responsible scale-up: methodological standardization, inclusive multilingual datasets, and governance frameworks that treat voice data as sensitive biometric medical information.